TITLE:
A Machine Learning Approach to Predicting Treatment Outcomes in Bipolar Depression with OCD Comorbidity
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Bipolar Depression, Obsessive-Compulsive Disorder (OCD), Machine Learning (ML), XGBoost, Treatment Response Prediction, SHAP (SHapley Additive exPlanations)
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.2,
February
28,
2025
ABSTRACT: Bipolar depression with comorbid obsessive-compulsive disorder (OCD) presents a significant clinical challenge due to its complex symptomatology, unpredictable treatment responses, and high relapse rates. Traditional approaches to treatment planning lack reliable tools for predicting patient-specific outcomes, leaving clinicians with limited options for personalizing care. This study leverages advanced machine learning (ML), specifically XGBoost, to develop a predictive framework capable of classifying treatment responses while identifying key predictors such as age, clinical scores (HDRS, YBOCS), and treatment characteristics (quetiapine dose). By incorporating interpretability techniques such as SHAP (SHapley Additive exPlanations), the model provides transparent insights into how individual features influence predictions, making the outputs actionable for clinical decision-making. Furthermore, probabilistic predictions are evaluated and calibrated using isotonic regression to ensure reliability, particularly for high-stakes applications in psychiatry. Through detailed visual analyses, including confusion matrices, ROC-AUC curves, SHAP plots, and calibration curves, this research bridges the gap between data-driven methodologies and clinical practice, offering a robust framework for advancing personalized treatment strategies in bipolar depression with OCD comorbidity.Subject AreasPsychiatry, Machine Learning, Healthcare